Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1-4. doi: 10.1109/EMBC48229.2022.9871492.
With growing size of resting state fMRI datasets and advances in deep learning methods, there are ever increasing opportunities to leverage progress in deep learning to solve challenging tasks in neuroimaging. In this work, we build upon recent advances in deep metric learning, to learn embeddings of rs-fMRI data, which can then be potentially used for several downstream tasks. We propose an efficient training method for our model and compare our method with other widely used models. Our experimental results indicate that deep metric learning can be used as an additional refinement step to learn representations of fMRI data, that significantly improves performance on downstream modeling tasks.
随着静息态 fMRI 数据集的规模不断增大和深度学习方法的不断进步，利用深度学习的进展来解决神经影像学中的挑战性任务的机会越来越多。在这项工作中，我们基于深度学习度量学习的最新进展，学习 rs-fMRI 数据的嵌入，然后这些嵌入可以潜在地用于多个下游任务。我们提出了一种有效的训练方法，并将我们的方法与其他广泛使用的模型进行了比较。我们的实验结果表明，深度学习可以作为学习 fMRI 数据表示的附加细化步骤，显著提高下游建模任务的性能。
